Classical and metaheuristic optimizations performance in an electro-hydraulic control system

Electro-Hydraulic Actuator (EHA) system is a prevalent mechanism in industrial sectors. This system commonly involving works that required high force such as steel, automotive and aerospace industries. It is a challenging task to acquire precision when dealing with a system that can produce high force. Besides, since most of the mechanical actuator performance varies with time, it is even difficult to ensure its robustness characteristic towards time. Therefore, this paper proposed the industrial’s wellknown controller, which is the Proportional-Integral-Derivative (PID) controller that can improve the precision of the EHA system. Then, an enhanced PID controller, which is the fractional order PID (FOPID) controller will be applied. A classical and metaheuristic optimization methods, which are gradient descent (GD) and particle swarm optimization (PSO) algorithm are used to obtaining the optimal gains of both controllers. In addition, to examine the tracking performance of the designed controllers, the performance of the proposed optimization algorithms is analysed. As a result, in a practical point of view, it can be inferred that the PSO algorithm is capable to generate more practical sense of gains compared with GD, and the precision characteristic of the FOPID is greater than the PID controller

A novel PID-like particle swarm optimizer: on terminal convergence analysis

Copyright © 2021 The Author(s). In this paper, a novel proportion-integral-derivative-like particle swarm optimization (PIDLPSO) algorithm is presented with improved terminal convergence of the particle dynamics. A derivative control term is introduced into the traditional particle swarm optimization (PSO) algorithm so as to alleviate the overshoot problem during the stage of the terminal convergence. The velocity of the particle is updated according to the past momentum, the present positions (including the personal best position and the global best position), and the future trend of the positions, thereby accelerating the terminal convergence and adjusting the search direction to jump out of the area around the local optima. By using a combination of the Routh stability criterion and the final value theorem of the Z-transformation, the convergence conditions are obtained for the developed PIDLPSO algorithm. Finally, the experiment results reveal the superiority of the designed PIDLPSO algorithm over several other state-of-the-art PSO variants in terms of the population diversity, searching ability and convergence rate.National Natural Science Foundation of China under Grants 61873148, 61933007 and 620730070; AHPU Youth Top-notch Talent Support Program of China under Grant 2018BJRC009; Natural Science Foundation of Anhui Province of China under Grant 2108085MA07; Royal Society of the UK; Alexander von Humboldt Foundation of Germany